Background threads (graph building, simulation prep, report generation,
profile generation) now inherit the requesting user's locale preference.
Previously these fell back to 'zh' because Flask request context was
unavailable in spawned threads.
Adds a canonical machine-readable probability signal endpoint that distils
a completed simulation report into a structured prediction thesis.
New: backend/app/services/signal_extractor.py
- SignalExtractor.extract() calls chat_json() (3 attempts, temp 0.1, step 0.05)
against the report markdown and returns a validated MiroSignal dataclass
- Validates and normalises all fields: p_yes clamped to [0.01, 0.99],
confidence/action enums enforced, action recomputed from p_yes when invalid
- _trim_report() keeps the tail (conclusions) for long reports to stay within
token limits
- _salvage() fallback_parser recovers a minimal signal from partial LLM output
using regex probability extraction
New endpoint: POST /api/report/<report_id>/signal
- 404 if report not found
- 400 if report not yet completed or content is empty
- 422 if LLM fails after all retry attempts
- Returns canonical signal with thesis.{p_yes, confidence, action, regime,
summary, drivers, invalidators} — schema_version 1.1
New: backend/tests/services/test_signal_extractor.py
- 27 tests covering happy path, field validation/normalisation, report
trimming, _salvage fallback, and LLM failure propagation
- No real API calls — LLMClient fully mocked
Closes#277
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Introduced a unique report ID generation mechanism to enhance tracking and management of reports.
- Implemented detailed logging for the report generation process, including agent actions, planning stages, and tool calls, improving traceability and debugging.
- Added new API endpoints for retrieving agent and console logs, allowing users to access detailed execution logs and console outputs during report generation.
- Enhanced the frontend GraphPanel component with a notification for users when simulations finish, improving user experience and feedback.
- Introduced the Report Agent module to facilitate the automatic generation of simulation analysis reports using LangChain and Zep, following the ReACT model.
- Added functionality for report outline planning, segmented content generation, and user interaction through a dialogue interface.
- Implemented new API endpoints for report generation, status checking, and retrieval, enhancing the overall reporting capabilities.
- Updated README.md to include detailed instructions on the new report generation features and API usage.
- Enhanced the project structure to accommodate the new report management functionalities, including report storage and retrieval mechanisms.
Laura Roganovic
ghostubborn
LoryGlory
666ghj